Weekly Exercises #3

library(tidyverse)     # for graphing and data cleaning
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(ggthemes)      # for even more plotting themes
library(geofacet)      # for special faceting with US map layout
theme_set(theme_minimal())       # My favorite ggplot() theme :)

# Lisa's garden data
data("garden_harvest")

# Seeds/plants (and other garden supply) costs
data("garden_spending")

# Planting dates and locations
data("garden_planting")

# Tidy Tuesday dog breed data
breed_traits <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_traits.csv')
trait_description <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/trait_description.csv')
breed_rank_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_rank.csv')

# Tidy Tuesday data for challenge problem
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')

Setting up on GitHub!

Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 3rd weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises with garden data

These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

1. Summarize the garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.

garden_harvest %>% 
  mutate(day = wday(date, label = TRUE)) %>% 
  group_by(vegetable, day) %>% 
  summarize(total_weight_lbs = 0.00220462* sum(weight)) %>% 
  ungroup() %>% 
  pivot_wider(id_cols = vegetable, 
              names_from = day,
              values_from = total_weight_lbs, 
              values_fill = 0)

2. Summarize the garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?

garden_harvest %>% 
  group_by(vegetable, variety) %>% 
  summarize(tot_weight_lbs = 0.00220462* sum(weight)) %>% 
  left_join(garden_planting, 
            by = c("vegetable", "variety"))

The problem is that some varieties of vegetables are planted in more than one location. It would be more efficient to have one instance of each variety and have multiple plots in the plot column. Perhaps we could mutate the garden planting dataframe to combine each instance of a variety into one.

3. I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.

First, you would create a dataframe that contains the wholefoods prices for each vegetable and variety of vegetable that are from the garden_harvest and garden_spending dataframes. Once that is created you would join the garden_spending and the new wholefoods dataframe you created. Once those are merged, you could calculate the amount you spent on each vegetable, along with a price per pound amount. Then you could join that dateframe together with the garden_harvest dataframe. Now that all the information you need is in one place, you can calculate how much it would have cost to buy the same amount of vegetables that you produced using your price per pound and total weight amounts and then subtract the cost to plant each vegetable to find how much you would save by gardening.

4. Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.CHALLENGE: add the date near the end of the bar. (This is probably not a super useful graph because it’s difficult to read. This is more an exercise in using some of the functions you just learned.)

garden_harvest %>% 
  filter(vegetable %in% c("tomatoes")) %>% 
  group_by(variety, date) %>% 
  summarize(tot_weight_lbs = 0.00220462* sum(weight)) %>% 
  ggplot(aes(x = tot_weight_lbs, y = fct_reorder(variety, date, min, .desc = TRUE))) +
  geom_bar(stat = "identity") +
  ggtitle("Tomato Harvests Ordered by First Harvest Date") +
  labs(x = "Total Harvest Weight (lbs)", y = "Variety of Tomato") +
  theme(plot.title = element_text(hjust = 0.5))

5. In the garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().

garden_harvest %>% 
  mutate(lowercase_variety = str_to_lower(variety), variety_name_length = str_length(lowercase_variety)) %>% 
  group_by(vegetable, variety) %>% 
  arrange(vegetable, variety_name_length) %>% 
  distinct(variety, .keep_all = TRUE)

6. In the garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().

garden_harvest %>% 
  mutate(hasERorAR = str_detect(str_to_lower(variety), "er") | str_detect(str_to_lower(variety), "ar")) %>% 
  filter(hasERorAR == TRUE) %>% 
  distinct(variety)

Bicycle-Use Patterns

In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.

A typical Capital Bikeshare station. This one is at Florida and California, next to Pleasant Pops.

One of the vans used to redistribute bicycles to different stations.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usual, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.

Temporal patterns

It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:

7. A density plot, which is a smoothed out histogram, of the events versus sdate. Use geom_density().

Trips %>% 
  ggplot(aes(x = sdate)) +
  geom_density() +
  labs(x = "Date Bike Rentals Started", y = "Density") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals")

The density plot shows me that, generally, as it gets colder in DC (it moves towards the winter months), there are less bike rentals. Some of the spikes could correlate with holidays or times where DC become busier for whatever reason.

8. A density plot of the events versus time of day. You can use mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.

Trips %>% 
  mutate(time = hour(sdate) + (minute(sdate)/60)) %>% 
  ggplot(aes(x = time)) +
  geom_density() +
  labs(x = "Time Bike Rentals Started", y = "Density") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals")

This density plot tells me that the peak bike riding times are before and after the majority of people’s jobs. The spikes are around 7-8 a.m., as people commute to work, and from 5-6 p.m., as people go home or out for after work events.

9. A bar graph of the events versus day of the week. Put day on the y-axis.

Trips %>% 
  mutate(date = date(sdate), day = wday(date, label = TRUE)) %>% 
  group_by(day) %>% 
  ggplot(aes(y = day)) +
  geom_bar() +
  ggtitle("Bike Rentals by Day") +
  labs(x = "Number of Rentals", y = "Day of the Week") +
  theme(plot.title = element_text(hjust = 0.5))

This bar chart shows me that people, on average, rent bikes more during the week. This would line up with my earlier observation that many people use the bikes before and after work to beat rush hour traffic.

10. Facet your graph from exercise 8. by day of the week. Is there a pattern?

Trips %>% 
  mutate(time = hour(sdate) + (minute(sdate)/60), 
         date = date(sdate), 
         day = wday(date, label = TRUE)) %>% 
  ggplot(aes(x = time)) +
  geom_density() +
  labs(x = "Time Bike Rentals Started", y = "Density") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals by Day") +
  facet_wrap(~day)

It is very interesting to see the start contrast between weekdays and weekends. I notice that the weekdays follow the same general trend of the majority of rentals being before and after work. The weekend, however, has a more even spread throughout the day as I would guess most people are out and about exploring the city.

The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.

11. Change the graph from exercise 10 to set the fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.

Trips %>% 
  mutate(time = hour(sdate) + (minute(sdate)/60), 
         date = date(sdate), 
         day = wday(date, label = TRUE)) %>% 
  ggplot(aes(x = time, fill = client)) +
  geom_density(color = NA, alpha = 0.5) +
  labs(x = "Time Bike Rentals Started", y = "Density", fill = "Client Type") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals by Day") +
  facet_wrap(~day)

This faceted plot shows me that clients who are registered are the people that most strictly follow the typically work day schedule. I would guess these are mostly residents of DC commuting to work. On the other hand, the causal clients are most likely tourists that are just using the bikes to site see while they are visiting the city.

12. Change the previous graph by adding the argument position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?

Trips %>% 
  mutate(time = hour(sdate) + (minute(sdate)/60), 
         date = date(sdate), 
         day = wday(date, label = TRUE)) %>% 
  ggplot(aes(x = time, fill = client)) +
  geom_density(color = NA, alpha = 0.5, position = position_stack()) +
  labs(x = "Time Bike Rentals Started", y = "Density", fill = "Client Type") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals by Day") +
  facet_wrap(~day)

I think that the change made the plot look better because there is no overlap where you have to look through one color to see the other. The downside is that it is a way more confusing way to make the same plot as in number 10. It is hard to compare the plots from this question to that of the previous question since they do not represent the same things.

13. In this graph, go back to using the regular density plot (without position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.

Trips %>% 
  mutate(time = hour(sdate) + (minute(sdate)/60), 
         date = date(sdate), 
         day = wday(date, label = TRUE), 
         weekend = ifelse(day == "Sat" |day == "Sun", "weekend", "weekday")) %>% 
  ggplot(aes(x = time, fill = client)) +
  geom_density(color = NA, alpha = 0.5) +
  labs(x = "Time Bike Rentals Started", y = "Density", fill = "Client Type") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals by Time of Day") +
  facet_wrap(~weekend)

This plot confirms my suspicions that the registered clients are the ones that follow the typical work schedule, while the casual clients are more dispersed throughout the day. The weekend, however, you see almost the same distribution of both types of clients throughout the day as most people have off of work on Saturday and Sunday.

14. Change the graph from the previous problem to facet on client and fill with weekend. What information does this graph tell you that the previous didn’t? Is one graph better than the other?

Trips %>% 
  mutate(time = hour(sdate) + (minute(sdate)/60), 
         date = date(sdate), 
         day = wday(date, label = TRUE), 
         weekend = ifelse(day == "Sat" | day == "Sun", "weekend", "weekday")) %>% 
  ggplot(aes(x = time, fill = weekend)) +
  geom_density(color = NA, alpha = 0.5) +
  labs(x = "Time Bike Rentals Started", y = "Density", fill = "Weekend") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Density Plot of Bike Rentals by Client Type") +
  facet_wrap(~client)

I think that this plot is a better way to represent the difference in rental times by type of client. The division by client type makes the observations I have previously made more clear.

Spatial patterns

15. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!

Trips %>% 
  left_join(Stations, 
            by = c("sstation" = "name")) %>% 
  group_by(sstation, lat, long) %>% 
  mutate(count = length(`lat`[`lat`])) %>% 
  ggplot(aes(x = long, y = lat)) +
  geom_point(aes(color = count)) +
  labs(x = "Longitude", y = "Latitude", color = "Number of Trips") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Number of Trips by Station")

16. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? (Again, we’ll improve this next week when we learn about maps).

Trips %>% 
  left_join(Stations, 
            by = c("sstation" = "name")) %>% 
  group_by(sstation, lat, long) %>% 
  mutate(count = length(`lat`[`lat`]), 
            percent_casual = (length(`client`[`client` == "Casual"])/count) * 100) %>% 
  ggplot(aes(x = long, y = lat)) +
  geom_point(aes(color = percent_casual)) +
  labs(x = "Longitude", y = "Latitude", color = "Percent of Casual Riders") +
  theme(plot.title = element_text(hjust = 0.5)) +
  ggtitle("Percent of Trips by Casual Riders by Station")

Given the fact that I drive through DC on a pretty regular basis, I would guess that the majority of the higher casual rentals are near the big landmark tourist spots, like the National Zoo that I believe would be represented by the clump in the top left corner of the map. The spots in the middle clump with the lightest blue are near the White House, Washington Monument, and the National Mall.

DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.

Dogs!

In this section, we’ll use the data from 2022-02-01 Tidy Tuesday. If you didn’t use that data or need a little refresher on it, see the website.

17. The final product of this exercise will be a graph that has breed on the y-axis and the sum of the numeric ratings in the breed_traits dataset on the x-axis, with a dot for each rating. First, create a new dataset called breed_traits_total that has two variables – Breed and total_rating. The total_rating variable is the sum of the numeric ratings in the breed_traits dataset (we’ll use this dataset again in the next problem). Then, create the graph just described. Omit Breeds with a total_rating of 0 and order the Breeds from highest to lowest ranked. You may want to adjust the fig.height and fig.width arguments inside the code chunk options (eg. {r, fig.height=8, fig.width=4}) so you can see things more clearly - check this after you knit the file to assure it looks like what you expected.

breed_traits_total <- breed_traits %>% 
  group_by(Breed) %>%
  pivot_longer(cols = c(`Affectionate With Family`:`Drooling Level`, `Openness To Strangers`:`Mental Stimulation Needs`),
               names_to = "Rating Type",
               values_to = "Rating Number") %>% 
  mutate(total_rating = sum(`Rating Number`)) %>% 
  filter(total_rating > 0) %>% 
  select(`Breed`, `total_rating`) %>% 
  distinct()

breed_traits_total %>% 
  ggplot(aes(x = total_rating, y = fct_reorder(Breed, total_rating, max))) +
  geom_point() +
  theme(axis.text.y=element_text( hjust=1, vjust=0.5),
        text=element_text(size=15), 
        plot.title = element_text(hjust = 0.5)) +
  labs(x = "Total Breed Ranking", y = "Breed") +
  ggtitle("Total Ranks of Dog Breeds")

18. The final product of this exercise will be a graph with the top-20 dogs in total ratings (from previous problem) on the y-axis, year on the x-axis, and points colored by each breed’s ranking for that year (from the breed_rank_all dataset). The points within each breed will be connected by a line, and the breeds should be arranged from the highest median rank to lowest median rank (“highest” is actually the smallest number, eg. 1 = best). After you’re finished, think of AT LEAST one thing you could you do to make this graph better. HINTS: 1. Start with the breed_rank_all dataset and pivot it so year is a variable. 2. Use the separate() function to get year alone, and there’s an extra argument in that function that can make it numeric. 3. For both datasets used, you’ll need to str_squish() Breed before joining.

breed_traits_total %>% 
  mutate(Breed2 = str_squish(Breed)) %>% 
  left_join(breed_rank_all %>% 
              mutate(Breed2 = str_squish(Breed)), 
            by = c("Breed2")) %>% 
  arrange(-total_rating) %>% 
  slice(1:20) %>% 
  pivot_longer(`2013 Rank`:`2020 Rank`, 
               names_to = "name", 
               values_to = "value")  %>% 
  group_by(Breed2) %>% 
  mutate(year = parse_number(name), 
         median_rank = median(value)) %>% 
  ggplot(aes(x =  year, y = fct_reorder(Breed2, median_rank, .desc = TRUE))) +
  geom_line() +
  geom_point(aes(color = value)) +
  ggtitle("Top 20 Total Ranked Dogs") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Year", y = "Breed by Median Rank", color = "Breed Rank") +
  theme_classic() +
  scale_x_continuous(expand = c(0,0), limits = c(2012, 2021))

I think it would look kind of cool if the lines in between each point were a fade of the two point colors. I think it would make it easier to see the transition of the breed rank between years.

19. Create your own! Requirements: use a join or pivot function (or both, if you’d like), a str_XXX() function, and a fct_XXX() function to create a graph using any of the dog datasets. One suggestion is to try to improve the graph you created for the Tidy Tuesday assignment. If you want an extra challenge, find a way to use the dog images in the breed_rank_all file - check out the ggimage library and this resource for putting images as labels.

top_ten_dogs <- breed_rank_all %>% 
  pivot_longer(`2015 Rank`:`2020 Rank`) %>% 
  filter(value <= 10) %>% 
  mutate(name = parse_number(name), 
         Breed2 = str_squish(Breed), 
         Breed3 = fct_recode(Breed2, 
                             `German Shorthaired Pointers` = "Pointers (German Shorthaired)", 
                             `Golden Retrievers` = "Retrievers (Golden)", 
                             `Labrador Retrievers` = "Retrievers (Labrador)"))

top_ten_dogs %>% 
  ggplot() +
  geom_line(aes(x = name, y = value, color = Breed3)) +
  ggtitle("Top 10 Dog Breeds Every Year Since 2015") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Year", y = "Rank", color = "Breed") +
  theme_classic() +
  scale_y_reverse(n.breaks = 10) + 
  scale_x_continuous(expand = c(0,0), limits = c(2015, 2020))

GitHub link

20. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 03_exercises.Rmd, provide a link to the 03_exercises.md file, which is the one that will be most readable on GitHub.

Github Link: here

.md Link: here

Challenge problem!

This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.

21. In this exercise, you are going to try to replicate the graph below, created by Georgios Karamanis. I’m sure you can find the exact code on GitHub somewhere, but DON’T DO THAT! You will only be graded for putting an effort into this problem. So, give it a try and see how far you can get without doing too much googling. HINT: use facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.

kids %>% 
  filter(year %in% c(1997, 2016)) %>% 
  group_by(state) %>% 
  ggplot(aes(x = year, y = inf_adj_perchild)) +
  geom_line() +
  facet_geo(~state) +
  ggtitle("Change in Public Spending") +
  theme(plot.title = element_text(hjust = 0.5)) +
  labs(x = "Year", y = "Variable Adjusted for Inflation, per Child") +
  theme_void() +
  scale_color_identity() +
  theme(panel.background = element_rect(fill = "#516c96"), 
        plot.background = element_rect(fill = "#516c96"))

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?